Contextual Auto-Completion for Assistant Systems

ABSTRACT

In one embodiment, a method includes receiving a first user input from a first user, wherein the first user input comprises a partial request, presenting one or more suggested intent auto-completions corresponding to the partial request, receiving a selection by the first user of a first suggested intent auto-completion of the suggested intent auto-completions and a second user input, presenting one or more suggested slot auto-completions corresponding to one or more candidate slot-hypotheses corresponding to the second user input, respectively, wherein each of the candidate slot-hypotheses comprise a slot-suggestion, and wherein each suggested slot auto-completion comprises the second user input and the corresponding candidate slot-hypothesis, receiving a selection by the first user of a first suggested slot auto-completion of the suggested slot auto-completions, and presenting execution results of one or more tasks corresponding to the first suggested intent auto-completion and the first suggested slot auto-completion.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 16/150,069, filed 2 Oct. 2018, which claims thebenefit, under 35 U.S.C. § 119(e), of U.S. Provisional PatentApplication No. 62/660,876, filed 20 Apr. 2018, which is incorporatedherein by reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, or a combination of them. The assistantsystem may perform concierge-type services (e.g., making dinnerreservations, purchasing event tickets, making travel arrangements) orprovide information based on the user input. The assistant system mayalso perform management or data-handling tasks based on onlineinformation and events without user initiation or interaction. Examplesof those tasks that may be performed by an assistant system may includeschedule management (e.g., sending an alert to a dinner date that a useris running late due to traffic conditions, update schedules for bothparties, and change the restaurant reservation time). The assistantsystem may be enabled by the combination of computing devices,application programming interfaces (APIs), and the proliferation ofapplications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video) in stateful and multi-turn conversations to getassistance. The assistant system may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system may analyze theuser input using natural-language understanding. The analysis may bebased on the user profile for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may use dialogmanagement techniques to manage and forward the conversation flow withthe user. In particular embodiments, the assistant system may furtherassist the user to effectively and efficiently digest the obtainedinformation by summarizing the information. The assistant system mayalso assist the user to be more engaging with an online social networkby providing tools that help the user interact with the online socialnetwork (e.g., creating posts, comments, messages). The assistant systemmay additionally assist the user to manage different tasks such askeeping track of events. In particular embodiments, the assistant systemmay proactively execute tasks that are relevant to user interests andpreferences based on the user profile without a user input. Inparticular embodiments, the assistant system may check privacy settingsto ensure that accessing a user's profile or other user information andexecuting different tasks are permitted subject to the user's privacysettings.

In particular embodiments, the assistant system may suggestcontextually-relevant typeahead-like auto-completions to users. Theassistant system may receive user input in various modalities includingaudio, text, video, images, etc. In public settings, some modalities maybe inconvenient and/or inappropriate to use (e.g., audio and video) andusers may prefer to enter textual input into the assistant system toprotect their privacy. One challenge with textual input may be thatkeypad entry is slower than audio/speech input for a typical user.Accordingly, ways to increase the speed of keypad entry would bebeneficial for improving the user interaction with the assistant system.In particular embodiments, the assistant system may leverage apersonalized language model that predicts a next keypad entry (e.g.,character, word, phrase, sentence, etc.) to generate suggestedauto-completions that help users complete their entries more quickly andwith less effort. As an example and not by way of limitation, if a useris interacting with the assistant system via a messaging interface andhas typed in “call . . . ”, the assistant system may determine the inputcorresponds to the intent [IN:call(person)]. The personalized languagemodel may then predict that the next keypad entry is a person's name tofill the slot [SL:person(name)]. The entries for the suggestedauto-completions may be stored in a range trie that indexes entries toallow efficient look-up given a prefix. Accordingly, the personalizedlanguage model may select a list of entries for the suggestedauto-completions from the range trie. The user may further select asuggested auto-completion, thus reducing the number of keystrokes thatthe user needs to enter to complete a request that can be executed bythe assistant system. Although this disclosure describes suggestingparticular auto-completions via particular systems in particularmanners, this disclosure contemplates suggesting any suitableauto-completion via any suitable system in any suitable manner.

In particular embodiments, the assistant system may receive, from aclient system associated with a first user, a user input from the firstuser. The user input may comprise a partial request. In particularembodiments, the assistant system may analyze, based on a personalizedlanguage model, the user input to generate one or more candidatehypotheses corresponding to the partial request. Each of the one or morecandidate hypotheses may comprise one or more of an intent-suggestion ora slot-suggestion. Each of the one or more candidate hypotheses mayadditionally correspond to a subsequent entry associated with the userinput. In particular embodiments, the assistant system may send, to theclient system, instructions for presenting one or more suggestedauto-completions corresponding to one or more of the candidatehypotheses, respectively. Each suggested auto-completion may comprisethe partial request and the corresponding candidate hypothesis. Inparticular embodiments, the assistant system may receive, from theclient system, an indication of a selection by the first user of a firstsuggested auto-completion of the one or more suggested auto-completions.The assistant system may further execute, via one or more agents, one ormore tasks based on the first suggested auto-completion selected by thefirst user.

Certain technical challenges may exist for achieving the goal ofsuggesting correct auto-completions responsive to a partial request of auser input by assistant systems. One technical challenge may includeaccurately determining candidate hypotheses based on the partialrequest. The solution presented by the embodiments disclosed herein toaddress the above challenge is using a personalized language model basedon a recurrent neural network which is trained based on a variety oftraining data associated with a user, which is discriminating indetermining candidate hypotheses since various information about theuser is learned from the training data to lead to a comprehensiveunderstanding of the partial request. Another technical challenge mayinclude presenting the most relevant candidate hypotheses to a user. Thesolutions presented by the embodiments disclosed herein to address thischallenge include ranking the candidate hypotheses based on a dialogstate and confidence scores determined by the personalized languagemodel, and dynamically updating the confidence scores based onadditional user input, which generate a list of ranked candidatehypotheses more correctly reflecting a user's intention in a currentdialog session. Another technical challenge may include generating thecandidate hypotheses in real time. The solution presented by theembodiments disclosed herein to address this challenge includes storingthe candidate hypotheses in a range trie as the assistant system canquickly look up candidate hypotheses in the range trie. Anothertechnical challenge may include the sparsity of data associated with anindividual user. The solution presented by the embodiments disclosedherein to address this challenge include using global language modelstrained from the data associated with a plurality of users as these dataare sufficient to learn discriminative language models which can enhancethe generation of candidate hypotheses by the personalized languagemodel alone.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includeimproving user interaction with the assistant system by helping userscomplete their typing more quickly and with less effort. Anothertechnical advantage of the embodiments may include guiding users totasks which the assistant system is capable of executing (for example,if the user does not know the capabilities of the assistant system) toimprove user experience with the assistant system. Certain embodimentsdisclosed herein may provide none, some, or all of the above technicaladvantages. One or more other technical advantages may be readilyapparent to one skilled in the art in view of the figures, descriptions,and claims of the present disclosure.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system.

FIG. 4 illustrates an example diagram flow of suggestingauto-completions based on the example architecture of the assistantsystem in FIG. 2.

FIG. 5A illustrates an example interaction with a user for suggestedauto-completions in a messaging interface.

FIG. 5B illustrates an example interaction with the user for suggestedauto-completions in the messaging interface after the user selected apreviously suggested auto-completion.

FIG. 6 illustrates an example method for suggesting auto-completions.

FIG. 7 illustrates an example social graph.

FIG. 8 illustrates an example view of an embedding space.

FIG. 9 illustrates an example artificial neural network.

FIG. 10 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, the client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. PatentApplication No. 62/655,751, filed 10 Apr. 2018, U.S. Design patentapplication Ser. No. 29/631,910, filed 3 Jan. 2018, U.S. Design patentapplication Ser. No. 29/631,747, filed 2 Jan. 2018, U.S. Design patentapplication Ser. No. 29/631,913, filed 3 Jan. 2018, and U.S. Designpatent application Ser. No. 29/631,914, filed 3 Jan. 2018, each of whichis incorporated by reference. This disclosure contemplates any suitableclient systems 130. A client system 130 may enable a network user at aclient system 130 to access a network 110. A client system 130 mayenable its user to communicate with other users at other client systems130.

In particular embodiments, a client system 130 may include a web browser132 and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, etc. The assistant application 136 may communicatethe user input to the assistant system 140. Based on the user input, theassistant system 140 may generate responses. The assistant system 140may send the generated responses to the assistant application 136. Theassistant application 136 may then present the responses to the user atthe client system 130. The presented responses may be based on differentmodalities such as audio, text, image, and video. As an example and notby way of limitation, the user may verbally ask the assistantapplication 136 about the traffic information (i.e., via an audiomodality). The assistant application 136 may then communicate therequest to the assistant system 140. The assistant system 140 mayaccordingly generate the result and send it back to the assistantapplication 136. The assistant application 136 may further present theresult to the user in text.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based onuser's profile and other relevant information. The result of theanalysis may comprise different entities associated with an onlinesocial network. The assistant system 140 may then retrieve informationor request services associated with these entities. In particularembodiments, the assistant system 140 may interact with thesocial-networking system 160 and/or third-party system 170 whenretrieving information or requesting services for the user. Inparticular embodiments, the assistant system 140 may generate apersonalized communication content for the user using natural-languagegenerating techniques. The personalized communication content maycomprise, for example, the retrieved information or the status of therequested services. In particular embodiments, the assistant system 140may enable the user to interact with it regarding the information orservices in a stateful and multi-turn conversation by usingdialog-management techniques. The functionality of the assistant system140 is described in more detail in the discussion of FIG. 2 below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, or a third-party system 170 to manage, retrieve, modify,add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow athird-party system 170 to access information from the social-networkingsystem 160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff the social-networking system 160. In conjunction with the actionlog, a third-party-content-object log may be maintained of userexposures to third-party-content objects. A notification controller mayprovide information regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from a client system 130 responsive to arequest received from a client system 130. Authorization servers may beused to enforce one or more privacy settings of the users of thesocial-networking system 160. A privacy setting of a user determines howparticular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by the social-networking system 160 or shared withother systems (e.g., a third-party system 170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 170. Location stores may be used for storinglocation information received from client systems 130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Assistant Systems

FIG. 2 illustrates an example architecture of the assistant system 140.In particular embodiments, the assistant system 140 may assist a user toobtain information or services. The assistant system 140 may enable theuser to interact with it with multi-modal user input (such as voice,text, image, video) in stateful and multi-turn conversations to getassistance. The assistant system 140 may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system 140 may analyzethe user input using natural-language understanding. The analysis may bebased on the user profile for more personalized and context-awareunderstanding. The assistant system 140 may resolve entities associatedwith the user input based on the analysis. In particular embodiments,the assistant system 140 may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system 140 may generate a response for the user regardingthe information or services by using natural-language generation.Through the interaction with the user, the assistant system 140 may usedialog management techniques to manage and forward the conversation flowwith the user. In particular embodiments, the assistant system 140 mayfurther assist the user to effectively and efficiently digest theobtained information by summarizing the information. The assistantsystem 140 may also assist the user to be more engaging with an onlinesocial network by providing tools that help the user interact with theonline social network (e.g., creating posts, comments, messages). Theassistant system 140 may additionally assist the user to managedifferent tasks such as keeping track of events. In particularembodiments, the assistant system 140 may proactively executepre-authorized tasks that are relevant to user interests and preferencesbased on the user profile, at a time relevant for the user, without auser input. In particular embodiments, the assistant system 140 maycheck privacy settings to ensure that accessing a user's profile orother user information and executing different tasks are permittedsubject to the user's privacy settings. More information on assistingusers subject to privacy settings may be found in U.S. PatentApplication No. 62/675,090, filed 22 May 2018, which is incorporated byreference.

In particular embodiments, the assistant system 140 may receive a userinput from the assistant application 136 in the client system 130associated with the user. In particular embodiments, the user input maybe a user generated input that is sent to the assistant system 140 in asingle turn. If the user input is based on a text modality, theassistant system 140 may receive it at a messaging platform 205. If theuser input is based on an audio modality (e.g., the user may speak tothe assistant application 136 or send a video including speech to theassistant application 136), the assistant system 140 may process itusing an audio speech recognition (ASR) module 210 to convert the userinput into text. If the user input is based on an image or videomodality, the assistant system 140 may process it using opticalcharacter recognition techniques within the messaging platform 205 toconvert the user input into text. The output of the messaging platform205 or the ASR module 210 may be received at an assistant xbot 215. Moreinformation on handling user input based on different modalities may befound in U.S. patent application Ser. No. 16/053,600, filed 2 Aug. 2018,which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may be a type of chatbot. The assistant xbot 215 may comprise a programmable service channel,which may be a software code, logic, or routine that functions as apersonal assistant to the user. The assistant xbot 215 may work as theuser's portal to the assistant system 140. The assistant xbot 215 maytherefore be considered as a type of conversational agent. In particularembodiments, the assistant xbot 215 may send the textual user input to anatural-language understanding (NLU) module 220 to interpret the userinput. In particular embodiments, the NLU module 220 may get informationfrom a user context engine 225 and a semantic information aggregator 230to accurately understand the user input. The user context engine 225 maystore the user profile of the user. The user profile of the user maycomprise user-profile data including demographic information, socialinformation, and contextual information associated with the user. Theuser-profile data may also include user interests and preferences on aplurality of topics, aggregated through conversations on news feed,search logs, messaging platform 205, etc. The usage of a user profilemay be protected behind a privacy check module 245 to ensure that auser's information can be used only for his/her benefit, and not sharedwith anyone else. More information on user profiles may be found in U.S.patent application Ser. No. 15/967,239, filed 30 Apr. 2018, which isincorporated by reference. The semantic information aggregator 230 mayprovide ontology data associated with a plurality of predefined domains,intents, and slots to the NLU module 220. In particular embodiments, adomain may denote a social context of interaction, e.g., education. Anintent may be an element in a pre-defined taxonomy of semanticintentions, which may indicate a purpose of a user interacting with theassistant system 140. In particular embodiments, an intent may be anoutput of the NLU module 220 if the user input comprises a text/speechinput. The NLU module 220 may classify the text/speech input into amember of the pre-defined taxonomy, e.g., for the input “PlayBeethoven's 5th,” the NLU module 220 may classify the input as havingthe intent [intent:play music]. In particular embodiments, a domain maybe conceptually a namespace for a set of intents, e.g., music. A slotmay be a named sub-string with the user input, representing a basicsemantic entity. For example, a slot for “pizza” may be [slot:dish]. Inparticular embodiments, a set of valid or expected named slots may beconditioned on the classified intent. As an example and not by way oflimitation, for [intent:play music], a slot may be [slot:song name]. Thesemantic information aggregator 230 may additionally extract informationfrom a social graph, a knowledge graph, and a concept graph, andretrieve a user's profile from the user context engine 225. The semanticinformation aggregator 230 may further process information from thesedifferent sources by determining what information to aggregate,annotating n-grams of the user input, ranking the n-grams withconfidence scores based on the aggregated information, formulating theranked n-grams into features that can be used by the NLU module 220 forunderstanding the user input. More information on aggregating semanticinformation may be found in U.S. patent application Ser. No. 15/967,342,filed 30 Apr. 2018, which is incorporated by reference. Based on theoutput of the user context engine 225 and the semantic informationaggregator 230, the NLU module 220 may identify a domain, an intent, andone or more slots from the user input in a personalized andcontext-aware manner. In particular embodiments, the NLU module 220 maycomprise a lexicon of language and a parser and grammar rules topartition sentences into an internal representation. The NLU module 220may also comprise one or more programs that perform naive semantics orstochastic semantic analysis to the use of pragmatics to understand auser input. In particular embodiments, the parser may be based on a deeplearning architecture comprising multiple long-short term memory (LSTM)networks. As an example and not by way of limitation, the parser may bebased on a recurrent neural network grammar (RNNG) model, which is atype of recurrent and recursive LSTM algorithm. More information onnatural-language understanding may be found in U.S. patent applicationSer. No. 16/011,062, filed 18 Jun. 2018, U.S. patent application Ser.No. 16/025,317, filed 2 Jul. 2018, and U.S. patent application Ser. No.16/038,120, filed 17 Jul. 2018, each of which is incorporated byreference.

In particular embodiments, the identified domain, intent, and one ormore slots from the NLU module 220 may be sent to a dialog engine 235.In particular embodiments, the dialog engine 235 may manage the dialogstate and flow of the conversation between the user and the assistantxbot 215. The dialog engine 235 may additionally store previousconversations between the user and the assistant xbot 215. In particularembodiments, the dialog engine 235 may communicate with an entityresolution module 240 to resolve entities associated with the one ormore slots, which supports the dialog engine 235 to forward the flow ofthe conversation between the user and the assistant xbot 215. Inparticular embodiments, the entity resolution module 240 may access thesocial graph, the knowledge graph, and the concept graph when resolvingthe entities. Entities may include, for example, unique users orconcepts, each of which may have a unique identifier (ID). As an exampleand not by way of limitation, the knowledge graph may comprise aplurality of entities. Each entity may comprise a single recordassociated with one or more attribute values. The particular record maybe associated with a unique entity identifier. Each record may havediverse values for an attribute of the entity. Each attribute value maybe associated with a confidence probability. A confidence probabilityfor an attribute value represents a probability that the value isaccurate for the given attribute. Each attribute value may be alsoassociated with a semantic weight. A semantic weight for an attributevalue may represent how the value semantically appropriate for the givenattribute considering all the available information. For example, theknowledge graph may comprise an entity of a movie “The Martian” (2015),which includes information that has been extracted from multiple contentsources (e.g., movie review sources, media databases, and entertainmentcontent sources), and then deduped, resolved, and fused to generate thesingle unique record for the knowledge graph. The entity may beassociated with a space attribute value which indicates the genre of themovie “The Martian” (2015). More information on the knowledge graph maybe found in U.S. patent application Ser. No. 16/048,049, filed 27 Jul.2018, and U.S. patent application Ser. No. 16/048,101, filed 27 Jul.2018, each of which is incorporated by reference. The entity resolutionmodule 240 may additionally request a user profile of the userassociated with the user input from the user context engine 225. Inparticular embodiments, the entity resolution module 240 may communicatewith a privacy check module 245 to guarantee that the resolving of theentities does not violate privacy policies. In particular embodiments,the privacy check module 245 may use an authorization/privacy server toenforce privacy policies. As an example and not by way of limitation, anentity to be resolved may be another user who specifies in his/herprivacy settings that his/her identity should not be searchable on theonline social network, and thus the entity resolution module 240 may notreturn that user's identifier in response to a request. Based on theinformation obtained from the social graph, knowledge graph, conceptgraph, and user profile, and subject to applicable privacy policies, theentity resolution module 240 may therefore accurately resolve theentities associated with the user input in a personalized andcontext-aware manner. In particular embodiments, each of the resolvedentities may be associated with one or more identifiers hosted by thesocial-networking system 160. As an example and not by way oflimitation, an identifier may comprise a unique user identifier (ID). Inparticular embodiments, each of the resolved entities may be alsoassociated with a confidence score. More information on resolvingentities may be found in U.S. patent application Ser. No. 16/048,049,filed 27 Jul. 2018, and U.S. patent application Ser. No. 16/048,072,filed 27 Jul. 2018, each of which is incorporated by reference.

In particular embodiments, the dialog engine 235 may communicate withdifferent agents based on the identified intent and domain, and theresolved entities. In particular embodiments, an agent may be animplementation that serves as a broker across a plurality of contentproviders for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. As an example and not byway of limitation, multiple device-specific implementations (e.g.,real-time calls for a client system 130 or a messaging application onthe client system 130) may be handled internally by a single agent.Alternatively, these device-specific implementations may be handled bymultiple agents associated with multiple domains. In particularembodiments, the agents may comprise first-party agents 250 andthird-party agents 255. In particular embodiments, first-party agents250 may comprise internal agents that are accessible and controllable bythe assistant system 140 (e.g. agents associated with services providedby the online social network). In particular embodiments, third-partyagents 255 may comprise external agents that the assistant system 140has no control over (e.g., music streams agents, ticket sales agents).The first-party agents 250 may be associated with first-party providers260 that provide content objects and/or services hosted by thesocial-networking system 160. The third-party agents 255 may beassociated with third-party providers 265 that provide content objectsand/or services hosted by the third-party system 170.

In particular embodiments, the communication from the dialog engine 235to the first-party agents 250 may comprise requesting particular contentobjects and/or services provided by the first-party providers 260. As aresult, the first-party agents 250 may retrieve the requested contentobjects from the first-party providers 260 and/or execute tasks thatcommand the first-party providers 260 to perform the requested services.In particular embodiments, the communication from the dialog engine 235to the third-party agents 255 may comprise requesting particular contentobjects and/or services provided by the third-party providers 265. As aresult, the third-party agents 255 may retrieve the requested contentobjects from the third-party providers 265 and/or execute tasks thatcommand the third-party providers 265 to perform the requested services.The third-party agents 255 may access the privacy check module 245 toguarantee no privacy violations before interacting with the third-partyproviders 265. As an example and not by way of limitation, the userassociated with the user input may specify in his/her privacy settingsthat his/her profile information is invisible to any third-party contentproviders. Therefore, when retrieving content objects associated withthe user input from the third-party providers 265, the third-partyagents 255 may complete the retrieval without revealing to thethird-party providers 265 which user is requesting the content objects.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may retrieve a user profile from the user contextengine 225 to execute tasks in a personalized and context-aware manner.As an example and not by way of limitation, a user input may comprise“book me a ride to the airport.” A transportation agent may execute thetask of booking the ride. The transportation agent may retrieve the userprofile of the user from the user context engine 225 before booking theride. For example, the user profile may indicate that the user preferstaxis, so the transportation agent may book a taxi for the user. Asanother example, the contextual information associated with the userprofile may indicate that the user is in a hurry so the transportationagent may book a ride from a ride-sharing service for the user since itmay be faster to get a car from a ride-sharing service than a taxicompany. In particular embodiment, each of the first-party agents 250 orthird-party agents 255 may take into account other factors whenexecuting tasks. As an example and not by way of limitation, otherfactors may comprise price, rating, efficiency, partnerships with theonline social network, etc.

In particular embodiments, the dialog engine 235 may communicate with aconversational understanding composer (CU composer) 270. The dialogengine 235 may send the requested content objects and/or the statuses ofthe requested services to the CU composer 270. In particularembodiments, the dialog engine 235 may send the requested contentobjects and/or the statuses of the requested services as a <k,c,u,d>tuple, in which k indicates a knowledge source, c indicates acommunicative goal, u indicates a user model, and d indicates adiscourse model. In particular embodiments, the CU composer 270 maycomprise a natural-language generator (NLG) 271 and a user interface(UI) payload generator 272. The natural-language generator 271 maygenerate a communication content based on the output of the dialogengine 235. In particular embodiments, the NLG 271 may comprise acontent determination component, a sentence planner, and a surfacerealization component. The content determination component may determinethe communication content based on the knowledge source, communicativegoal, and the user's expectations. As an example and not by way oflimitation, the determining may be based on a description logic. Thedescription logic may comprise, for example, three fundamental notionswhich are individuals (representing objects in the domain), concepts(describing sets of individuals), and roles (representing binaryrelations between individuals or concepts). The description logic may becharacterized by a set of constructors that allow the natural-languagegenerator 271 to build complex concepts/roles from atomic ones. Inparticular embodiments, the content determination component may performthe following tasks to determine the communication content. The firsttask may comprise a translation task, in which the input to thenatural-language generator 271 may be translated to concepts. The secondtask may comprise a selection task, in which relevant concepts may beselected among those resulted from the translation task based on theuser model. The third task may comprise a verification task, in whichthe coherence of the selected concepts may be verified. The fourth taskmay comprise an instantiation task, in which the verified concepts maybe instantiated as an executable file that can be processed by thenatural-language generator 271. The sentence planner may determine theorganization of the communication content to make it humanunderstandable. The surface realization component may determine specificwords to use, the sequence of the sentences, and the style of thecommunication content. The UI payload generator 272 may determine apreferred modality of the communication content to be presented to theuser. In particular embodiments, the CU composer 270 may communicatewith the privacy check module 245 to make sure the generation of thecommunication content follows the privacy policies. In particularembodiments, the CU composer 270 may retrieve a user profile from theuser context engine 225 when generating the communication content anddetermining the modality of the communication content. As a result, thecommunication content may be more natural, personalized, andcontext-aware for the user. As an example and not by way of limitation,the user profile may indicate that the user likes short sentences inconversations so the generated communication content may be based onshort sentences. As another example and not by way of limitation, thecontextual information associated with the user profile may indicatedthat the user is using a device that only outputs audio signals so theUI payload generator 272 may determine the modality of the communicationcontent as audio. More information on natural-language generation may befound in U.S. patent application Ser. No. 15/967,279, filed 30 Apr.2018, and U.S. patent application Ser. No. 15/966,455, filed 30 Apr.2018, each of which is incorporated by reference.

In particular embodiments, the CU composer 270 may send the generatedcommunication content to the assistant xbot 215. In particularembodiments, the assistant xbot 215 may send the communication contentto the messaging platform 205. The messaging platform 205 may furthersend the communication content to the client system 130 via theassistant application 136. In alternative embodiments, the assistantxbot 215 may send the communication content to a text-to-speech (TTS)module 275. The TTS module 275 may convert the communication content toan audio clip. The TTS module 275 may further send the audio clip to theclient system 130 via the assistant application 136.

In particular embodiments, the assistant xbot 215 may interact with aproactive inference layer 280 without receiving a user input. Theproactive inference layer 280 may infer user interests and preferencesbased on the user profile that is retrieved from the user context engine225. In particular embodiments, the proactive inference layer 280 mayfurther communicate with proactive agents 285 regarding the inference.The proactive agents 285 may execute proactive tasks based on theinference. As an example and not by way of limitation, the proactivetasks may comprise sending content objects or providing services to theuser. In particular embodiments, each proactive task may be associatedwith an agenda item. The agenda item may comprise a recurring item suchas a daily digest. The agenda item may also comprise a one-time item. Inparticular embodiments, a proactive agent 285 may retrieve the userprofile from the user context engine 225 when executing the proactivetask. Therefore, the proactive agent 285 may execute the proactive taskin a personalized and context-aware manner. As an example and not by wayof limitation, the proactive inference layer may infer that the userlikes the band Maroon 5 and the proactive agent 285 may generate arecommendation of Maroon 5's new song/album to the user.

In particular embodiments, the proactive agent 285 may generatecandidate entities associated with the proactive task based on a userprofile. The generation may be based on a straightforward backend queryusing deterministic filters to retrieve the candidate entities from astructured data store. The generation may be alternatively based on amachine-learning model that is trained based on the user profile, entityattributes, and relevance between users and entities. As an example andnot by way of limitation, the machine-learning model may be based onsupport vector machines (SVM). As another example and not by way oflimitation, the machine-learning model may be based on a regressionmodel. As another example and not by way of limitation, themachine-learning model may be based on a deep convolutional neuralnetwork (DCNN). In particular embodiments, the proactive agent 285 mayalso rank the generated candidate entities based on the user profile andthe content associated with the candidate entities. The ranking may bebased on the similarities between a user's interests and the candidateentities. As an example and not by way of limitation, the assistantsystem 140 may generate a feature vector representing a user's interestand feature vectors representing the candidate entities. The assistantsystem 140 may then calculate similarity scores (e.g., based on cosinesimilarity) between the feature vector representing the user's interestand the feature vectors representing the candidate entities. The rankingmay be alternatively based on a ranking model that is trained based onuser feedback data.

In particular embodiments, the proactive task may comprise recommendingthe candidate entities to a user. The proactive agent 285 may schedulethe recommendation, thereby associating a recommendation time with therecommended candidate entities. The recommended candidate entities maybe also associated with a priority and an expiration time. In particularembodiments, the recommended candidate entities may be sent to aproactive scheduler. The proactive scheduler may determine an actualtime to send the recommended candidate entities to the user based on thepriority associated with the task and other relevant factors (e.g.,clicks and impressions of the recommended candidate entities). Inparticular embodiments, the proactive scheduler may then send therecommended candidate entities with the determined actual time to anasynchronous tier. The asynchronous tier may temporarily store therecommended candidate entities as a job. In particular embodiments, theasynchronous tier may send the job to the dialog engine 235 at thedetermined actual time for execution. In alternative embodiments, theasynchronous tier may execute the job by sending it to other surfaces(e.g., other notification services associated with the social-networkingsystem 160). In particular embodiments, the dialog engine 235 mayidentify the dialog intent, state, and history associated with the user.Based on the dialog intent, the dialog engine 235 may select somecandidate entities among the recommended candidate entities to send tothe client system 130. In particular embodiments, the dialog state andhistory may indicate if the user is engaged in an ongoing conversationwith the assistant xbot 215. If the user is engaged in an ongoingconversation and the priority of the task of recommendation is low, thedialog engine 235 may communicate with the proactive scheduler toreschedule a time to send the selected candidate entities to the clientsystem 130. If the user is engaged in an ongoing conversation and thepriority of the task of recommendation is high, the dialog engine 235may initiate a new dialog session with the user in which the selectedcandidate entities may be presented. As a result, the interruption ofthe ongoing conversation may be prevented. When it is determined thatsending the selected candidate entities is not interruptive to the user,the dialog engine 235 may send the selected candidate entities to the CUcomposer 270 to generate a personalized and context-aware communicationcontent comprising the selected candidate entities, subject to theuser's privacy settings. In particular embodiments, the CU composer 270may send the communication content to the assistant xbot 215 which maythen send it to the client system 130 via the messaging platform 205 orthe TTS module 275. More information on proactively assisting users maybe found in U.S. patent application Ser. No. 15/967,193, filed 30 Apr.2018, and U.S. patent application Ser. No. 16/036,827, filed 16 Jul.2018, each of which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may communicate with aproactive agent 285 in response to a user input. As an example and notby way of limitation, the user may ask the assistant xbot 215 to set upa reminder. The assistant xbot 215 may request a proactive agent 285 toset up such reminder and the proactive agent 285 may proactively executethe task of reminding the user at a later time.

In particular embodiments, the assistant system 140 may comprise asummarizer 290. The summarizer 290 may provide customized news feedsummaries to a user. In particular embodiments, the summarizer 290 maycomprise a plurality of meta agents. The plurality of meta agents mayuse the first-party agents 250, third-party agents 255, or proactiveagents 285 to generated news feed summaries. In particular embodiments,the summarizer 290 may retrieve user interests and preferences from theproactive inference layer 280. The summarizer 290 may then retrieveentities associated with the user interests and preferences from theentity resolution module 240. The summarizer 290 may further retrieve auser profile from the user context engine 225. Based on the informationfrom the proactive inference layer 280, the entity resolution module240, and the user context engine 225, the summarizer 290 may generatepersonalized and context-aware summaries for the user. In particularembodiments, the summarizer 290 may send the summaries to the CUcomposer 270. The CU composer 270 may process the summaries and send theprocessing results to the assistant xbot 215. The assistant xbot 215 maythen send the processed summaries to the client system 130 via themessaging platform 205 or the TTS module 275. More information onsummarization may be found in U.S. patent application Ser. No.15/967,290, filed 30 Apr. 2018, which is incorporated by reference.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system 140. In particular embodiments, theassistant xbot 215 may access a request manager 305 upon receiving theuser request. The request manager 305 may comprise a context extractor306 and a conversational understanding object generator (CU objectgenerator) 307. The context extractor 306 may extract contextualinformation associated with the user request. The context extractor 306may also update contextual information based on the assistantapplication 136 executing on the client system 130. As an example andnot by way of limitation, the update of contextual information maycomprise content items are displayed on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise alarm is set on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise a song is playing on the client system 130. TheCU object generator 307 may generate particular content objects relevantto the user request. The content objects may comprise dialog-sessiondata and features associated with the user request, which may be sharedwith all the modules of the assistant system 140. In particularembodiments, the request manager 305 may store the contextualinformation and the generated content objects in data store 310 which isa particular data store implemented in the assistant system 140.

In particular embodiments, the request manger 305 may send the generatedcontent objects to the NLU module 220. The NLU module 220 may perform aplurality of steps to process the content objects. At step 221, the NLUmodule 220 may generate a whitelist for the content objects. Inparticular embodiments, the whitelist may comprise interpretation datamatching the user request. At step 222, the NLU module 220 may perform afeaturization based on the whitelist. At step 223, the NLU module 220may perform domain classification/selection on user request based on thefeatures resulted from the featurization to classify the user requestinto predefined domains. The domain classification/selection results maybe further processed based on two related procedures. At step 224 a, theNLU module 220 may process the domain classification/selection resultusing an intent classifier. The intent classifier may determine theuser's intent associated with the user request. In particularembodiments, there may be one intent classifier for each domain todetermine the most possible intents in a given domain. As an example andnot by way of limitation, the intent classifier may be based on amachine-learning model that may take the domain classification/selectionresult as input and calculate a probability of the input beingassociated with a particular predefined intent. At step 224 b, the NLUmodule may process the domain classification/selection result using ameta-intent classifier. The meta-intent classifier may determinecategories that describe the user's intent. In particular embodiments,intents that are common to multiple domains may be processed by themeta-intent classifier. As an example and not by way of limitation, themeta-intent classifier may be based on a machine-learning model that maytake the domain classification/selection result as input and calculate aprobability of the input being associated with a particular predefinedmeta-intent. At step 225 a, the NLU module 220 may use a slot tagger toannotate one or more slots associated with the user request. Inparticular embodiments, the slot tagger may annotate the one or moreslots for the n-grams of the user request. At step 225 b, the NLU module220 may use a meta slot tagger to annotate one or more slots for theclassification result from the meta-intent classifier. In particularembodiments, the meta slot tagger may tag generic slots such asreferences to items (e.g., the first), the type of slot, the value ofthe slot, etc. As an example and not by way of limitation, a userrequest may comprise “change 500 dollars in my account to Japanese yen.”The intent classifier may take the user request as input and formulateit into a vector. The intent classifier may then calculate probabilitiesof the user request being associated with different predefined intentsbased on a vector comparison between the vector representing the userrequest and the vectors representing different predefined intents. In asimilar manner, the slot tagger may take the user request as input andformulate each word into a vector. The intent classifier may thencalculate probabilities of each word being associated with differentpredefined slots based on a vector comparison between the vectorrepresenting the word and the vectors representing different predefinedslots. The intent of the user may be classified as “changing money”. Theslots of the user request may comprise “500”, “dollars”, “account”, and“Japanese yen”. The meta-intent of the user may be classified as“financial service”. The meta slot may comprise “finance”.

In particular embodiments, the NLU module 220 may improve the domainclassification/selection of the content objects by extracting semanticinformation from the semantic information aggregator 230. In particularembodiments, the semantic information aggregator 230 may aggregatesemantic information in the following way. The semantic informationaggregator 230 may first retrieve information from the user contextengine 225. In particular embodiments, the user context engine 225 maycomprise offline aggregators 226 and an online inference service 227.The offline aggregators 226 may process a plurality of data associatedwith the user that are collected from a prior time window. As an exampleand not by way of limitation, the data may include news feedposts/comments, interactions with news feed posts/comments, searchhistory, etc. that are collected from a prior 90-day window. Theprocessing result may be stored in the user context engine 225 as partof the user profile. The online inference service 227 may analyze theconversational data associated with the user that are received by theassistant system 140 at a current time. The analysis result may bestored in the user context engine 225 also as part of the user profile.In particular embodiments, both the offline aggregators 226 and onlineinference service 227 may extract personalization features from theplurality of data. The extracted personalization features may be used byother modules of the assistant system 140 to better understand userinput. In particular embodiments, the semantic information aggregator230 may then process the retrieved information, i.e., a user profile,from the user context engine 225 in the following steps. At step 231,the semantic information aggregator 230 may process the retrievedinformation from the user context engine 225 based on natural-languageprocessing (NLP). In particular embodiments, the semantic informationaggregator 230 may tokenize text by text normalization, extract syntaxfeatures from text, and extract semantic features from text based onNLP. The semantic information aggregator 230 may additionally extractfeatures from contextual information, which is accessed from dialoghistory between a user and the assistant system 140. The semanticinformation aggregator 230 may further conduct global word embedding,domain-specific embedding, and/or dynamic embedding based on thecontextual information. At step 232, the processing result may beannotated with entities by an entity tagger. Based on the annotations,the semantic information aggregator 230 may generate dictionaries forthe retrieved information at step 233. In particular embodiments, thedictionaries may comprise global dictionary features which can beupdated dynamically offline. At step 234, the semantic informationaggregator 230 may rank the entities tagged by the entity tagger. Inparticular embodiments, the semantic information aggregator 230 maycommunicate with different graphs 330 including social graph, knowledgegraph, and concept graph to extract ontology data that is relevant tothe retrieved information from the user context engine 225. Inparticular embodiments, the semantic information aggregator 230 mayaggregate the user profile, the ranked entities, and the informationfrom the graphs 330. The semantic information aggregator 230 may thensend the aggregated information to the NLU module 220 to facilitate thedomain classification/selection.

In particular embodiments, the output of the NLU module 220 may be sentto a co-reference module 315 to interpret references of the contentobjects associated with the user request. In particular embodiments, theco-reference module 315 may be used to identify an item the user requestrefers to. The co-reference module 315 may comprise reference creation316 and reference resolution 317. In particular embodiments, thereference creation 316 may create references for entities determined bythe NLU module 220. The reference resolution 317 may resolve thesereferences accurately. In particular embodiments, the co-referencemodule 315 may access the user context engine 225 and the dialog engine235 when necessary to interpret references with improved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution module 240 to resolve relevantentities. The entity resolution module 240 may execute generic anddomain-specific entity resolution. In particular embodiments, the entityresolution module 240 may comprise domain entity resolution 241 andgeneric entity resolution 242. The domain entity resolution 241 mayresolve the entities by categorizing the slots and meta slots intodifferent domains. In particular embodiments, entities may be resolvedbased on the ontology data extracted from the graphs 330. The ontologydata may comprise the structural relationship between differentslots/meta-slots and domains. The ontology may also comprise informationof how the slots/meta-slots may be grouped, related within a hierarchywhere the higher level comprises the domain, and subdivided according tosimilarities and differences. The generic entity resolution 242 mayresolve the entities by categorizing the slots and meta slots intodifferent generic topics. In particular embodiments, the resolving maybe also based on the ontology data extracted from the graphs 330. Theontology data may comprise the structural relationship between differentslots/meta-slots and generic topics. The ontology may also compriseinformation of how the slots/meta-slots may be grouped, related within ahierarchy where the higher level comprises the topic, and subdividedaccording to similarities and differences. As an example and not by wayof limitation, in response to the input of an inquiry of the advantagesof a car, the generic entity resolution 242 may resolve the car asvehicle and the domain entity resolution 241 may resolve the car aselectric car.

In particular embodiments, the output of the entity resolution module240 may be sent to the dialog engine 235 to forward the flow of theconversation with the user. The dialog engine 235 may comprise dialogintent resolution 236 and dialog state update/ranker 237. In particularembodiments, the dialog intent resolution 236 may resolve the userintent associated with the current dialog session based on dialoghistory between the user and the assistant system 140. The dialog intentresolution 236 may map intents determined by the NLU module 220 todifferent dialog intents. The dialog intent resolution 236 may furtherrank dialog intents based on signals from the NLU module 220, the entityresolution module 240, and dialog history between the user and theassistant system 140. In particular embodiments, the dialog stateupdate/ranker 237 may update/rank the dialog state of the current dialogsession. As an example and not by way of limitation, the dialog stateupdate/ranker 237 may update the dialog state as “completed” if thedialog session is over. As another example and not by way of limitation,the dialog state update/ranker 237 may rank the dialog state based on apriority associated with it.

In particular embodiments, the dialog engine 235 may communicate with atask completion module 335 about the dialog intent and associatedcontent objects. In particular embodiments, the task completion module335 may rank different dialog hypotheses for different dialog intents.The task completion module 335 may comprise an action selectioncomponent 336. In particular embodiments, the dialog engine 235 mayadditionally check against dialog policies 320 regarding the dialogstate. In particular embodiments, a dialog policy 320 may comprise adata structure that describes an execution plan of an action by an agent340. An agent 340 may select among registered content providers tocomplete the action. The data structure may be constructed by the dialogengine 235 based on an intent and one or more slots associated with theintent. A dialog policy 320 may further comprise multiple goals relatedto each other through logical operators. In particular embodiments, agoal may be an outcome of a portion of the dialog policy and it may beconstructed by the dialog engine 235. A goal may be represented by anidentifier (e.g., string) with one or more named arguments, whichparameterize the goal. As an example and not by way of limitation, agoal with its associated goal argument may be represented as{confirm_artist, args:{artist: “Madonna”}}. In particular embodiments, adialog policy may be based on a tree-structured representation, in whichgoals are mapped to leaves of the tree. In particular embodiments, thedialog engine 235 may execute a dialog policy 320 to determine the nextaction to carry out. The dialog policies 320 may comprise generic policy321 and domain specific policies 322, both of which may guide how toselect the next system action based on the dialog state. In particularembodiments, the task completion module 335 may communicate with dialogpolicies 320 to obtain the guidance of the next system action. Inparticular embodiments, the action selection component 336 may thereforeselect an action based on the dialog intent, the associated contentobjects, and the guidance from dialog policies 320.

In particular embodiments, the output of the task completion module 335may be sent to the CU composer 270. In alternative embodiments, theselected action may require one or more agents 340 to be involved. As aresult, the task completion module 335 may inform the agents 340 aboutthe selected action. Meanwhile, the dialog engine 235 may receive aninstruction to update the dialog state. As an example and not by way oflimitation, the update may comprise awaiting agents' response. Inparticular embodiments, the CU composer 270 may generate a communicationcontent for the user using the NLG 271 based on the output of the taskcompletion module 335. In particular embodiments, the NLG 271 may usedifferent language models and/or language templates to generate naturallanguage outputs. The generation of natural language outputs may beapplication specific. The generation of natural language outputs may bealso personalized for each user. The CU composer 270 may also determinea modality of the generated communication content using the UI payloadgenerator 272. Since the generated communication content may beconsidered as a response to the user request, the CU composer 270 mayadditionally rank the generated communication content using a responseranker 273. As an example and not by way of limitation, the ranking mayindicate the priority of the response.

In particular embodiments, the output of the CU composer 270 may be sentto a response manager 325. The response manager 325 may performdifferent tasks including storing/updating the dialog state 326retrieved from data store 310 and generating responses 327. Inparticular embodiments, the output of CU composer 270 may comprise oneor more of natural-language strings, speech, or actions with parameters.As a result, the response manager 325 may determine what tasks toperform based on the output of CU composer 270. In particularembodiments, the generated response and the communication content may besent to the assistant xbot 215. In alternative embodiments, the outputof the CU composer 270 may be additionally sent to the TTS module 275 ifthe determined modality of the communication content is audio. Thespeech generated by the TTS module 275 and the response generated by theresponse manager 325 may be then sent to the assistant xbot 215.

Contextual Auto-Completion for Assistant Systems

In particular embodiments, the assistant system 140 may suggestcontextually-relevant typeahead-like auto-completions to users. Theassistant system 140 may receive user input in various modalitiesincluding audio, text, video, images, etc. In public settings, somemodalities may be inconvenient and/or inappropriate to use (e.g., audioand video) and users may prefer to enter textual input into theassistant system 140 to protect their privacy. One challenge withtextual input may be that keypad entry is slower than audio/speech inputfor a typical user. Accordingly, ways to increase the speed of keypadentry would be beneficial for improving the user interaction with theassistant system 140. In particular embodiments, the assistant system140 may leverage a personalized language model that predicts a nextkeypad entry (e.g., character, word, phrase, sentence, etc.) to generatesuggested auto-completions that help users complete their entries morequickly and with less effort. As an example and not by way oflimitation, if a user is interacting with the assistant system 140 via amessaging interface and has typed in “call . . . ”, the assistant system140 may determine the input corresponds to the intent [IN:call(person)].The personalized language model may then predict that the next keypadentry is a person's name to fill the slot [SL:person(name)]. The entriesfor the suggested auto-completions may be stored in a range trie thatindexes entries to allow efficient look-up given a prefix. Accordingly,the personalized language model may select a list of entries for thesuggested auto-completions from the range trie. The user may furtherselect a suggested auto-completion, thus reducing the number ofkeystrokes that the user needs to enter to complete a request that canbe executed by the assistant system 140. Although this disclosuredescribes suggesting particular auto-completions via particular systemsin particular manners, this disclosure contemplates suggesting anysuitable auto-completion via any suitable system in any suitable manner.

In particular embodiments, the assistant system 140 may receive, from aclient system 130 associated with a first user, a user input from thefirst user. The user input may comprise a partial request. In particularembodiments, the assistant system 140 may analyze, based on apersonalized language model, the user input to generate one or morecandidate hypotheses corresponding to the partial request. Each of theone or more candidate hypotheses may comprise one or more of anintent-suggestion or a slot-suggestion. Each of the one or morecandidate hypotheses may additionally correspond to a subsequent entryassociated with the user input. In particular embodiments, the assistantsystem 140 may send, to the client system 130, instructions forpresenting one or more suggested auto-completions corresponding to oneor more of the candidate hypotheses, respectively. Each suggestedauto-completion may comprise the partial request and the correspondingcandidate hypothesis. In particular embodiments, the assistant system140 may receive, from the client system 130, an indication of aselection by the first user of a first suggested auto-completion of theone or more suggested auto-completions. The assistant system 140 mayfurther execute, via one or more agents, one or more tasks based on thefirst suggested auto-completion selected by the first user.

FIG. 4 illustrates an example diagram flow of suggestingauto-completions based on the example architecture of the assistantsystem 140 in FIG. 2. In particular embodiments, the assistant xbot 215may receive, from a client system 130 associated with a first user, auser input 405 from the first user. As an example and not by way oflimitation, the user input 405 may comprise a character string. Inparticular embodiments, the user input 405 may comprise a partialrequest. The partial request may comprise a non-executable request forwhich the assistant system 140 may not be able to instantly determinetasks associated with the request and execute the tasks. In particularembodiments, the assistant xbot 215 may send the user input 405 to thedialog engine 235. The dialog engine 235 may analyze, based on apersonalized language model 410, the user input 405 to generate one ormore candidate hypotheses 415 corresponding to the partial request. Thegeneration of the one or more candidate hypotheses 415 may be furtherbased on one or more context-specific language models 420, one or moreglobal language models 425, and one or more global context-specificlanguage models 430. In particular embodiments, the dialog engine 235may train the personalized language model 410 based on training data 435accessed from the user context engine 225. The dialog engine 235 maytrain the context-specific language models 420 based on context-specificdata 440 accessed from the user context engine 225. In particularembodiments, the dialog engine 235 may train the global language models445 based on global user data 450 accessed from the data store 164 ofthe social-networking system 160. The dialog engine 235 may train theglobal context-specific language models 430 based on globalcontext-specific user data 455 accessed from the data store 164 of thesocial-networking system 160. In particular embodiments, the global userdata 450 and global context-specific user data 455 are data associatedwith a plurality of users on an online social network. In particularembodiments, the dialog engine 235 may store the trained personalizedlanguage model 410 and context-specific language models 420 locally on auser's client system 130 to protect the user's privacy. The dialogengine 235 may then access the stored models from the user's clientsystem 130 subject to the user's permissions when analyzing the userinput 405 to generate candidate hypotheses 415. In particularembodiments, the dialog engine 235 may store the trained global languagemodels 425 and global context-specific language models 430 in one ormore data stores 310 of the assistant system 140 as these models do notcontain privacy-sensitive information of specific users. The dialogengine 235 may then access the stored models from the data stores 310when analyzing the user input 405 to generate candidate hypotheses 415.In particular embodiments, the generated candidate hypotheses 415 may besent back to the assistant xbot 215. The assistant xbot 215 may furthergenerate suggested auto-completions 460 corresponding to the candidatehypotheses 415. Each suggested auto-completion 460 may comprise thepartial request and the corresponding candidate hypothesis 415. Theassistant xbot 215 may further send, to the client system 130,instructions for presenting the suggested auto-completions 460 to thefirst user. In particular embodiments, the first user may select asuggested auto-completion 460 from the suggested auto-completions 460.The selection of the auto-completion 460 may transform the partialrequest to a complete request. Accordingly, the assistant system 140 maythen execute tasks associated with the complete request. As a result,the assistant system 140 may have a technical advantage of improvinguser interaction with the assistant system 140 by helping users completetheir typing more quickly and with less effort. Although this disclosuredescribes suggesting particular auto-completions using particularmodules in a particular manner, this disclosure contemplates suggestingany suitable auto-completions using any suitable modules in any suitablemanner.

In particular embodiments, each of the one or more candidate hypotheses415 may comprise one or more of an intent-suggestion or aslot-suggestion. The assistant system 140 may analyze the user input 405to generate one or more candidate hypotheses 415 comprising theintent-suggestion corresponding to the partial request in the followingway. The assistant system 140 may first analyze, based on thepersonalized language model 410, the user input 405 to determine one ormore candidate intents. In particular embodiments, the assistant system140 may then send, to the client system 130, one or moreintent-suggestions corresponding to the one or more candidate intents.The assistant system 140 may further receive, from the client system 130by the first user, a selection of one of the one or moreintent-suggestions. The selected intent suggestion may be subsequentlyprovided as an intent-suggestion for one of the candidate hypotheses415. In alternative embodiments, the assistant system 140 may provide anintent-suggestion upon determining the one or more candidate intents asfollows. The assistant system 140 may send, to the client system 130, arequest for additional information from the first user. The assistantsystem 140 may then receive, from the client system 130, an additionaluser input by the first user response to the request. The assistantsystem 140 may further disambiguate, based on the additional user input,the one or more candidate intents to determine a top candidate intent toprovide as an intent-suggestion for one of the candidate hypotheses 415.Although this disclosure describes providing particularintent-suggestions in particular manners, this disclosure contemplatesproviding any suitable intent-suggestions in any suitable manner.

In particular embodiments, the assistant system 140 may analyze the userinput 405 to generate one or more candidate hypotheses 415 comprisingthe slot-suggestion corresponding to the partial request in thefollowing way. The assistant system 140 may first analyze, based on thepersonalized language model 410, the user input 405 to determine one ormore candidate slots. In particular embodiments, the assistant system140 may then send, to the client system 130, one or moreslot-suggestions corresponding to the one or more candidate slots. Theassistant system 140 may further receive, from the client system 130 bythe first user, a selection of one of the one or more slot-suggestions.The selected slot suggestion may be subsequently provided as aslot-suggestion for one of the candidate hypotheses 415. In alternativeembodiments, the assistant system 140 may provide a slot-suggestion upondetermining the one or more candidate slots as follows. The assistantsystem 140 may send, to the client system 130, a request for additionalinformation from the first user. The assistant system 140 may thenreceive, from the client system 130, an additional user input by thefirst user response to the request. The assistant system 140 may furtherdisambiguate, based on the additional user input, the one or morecandidate slots to determine a top candidate slot to provide as aslot-suggestion for one of the candidate hypotheses 415. Although thisdisclosure describes providing particular slot-suggestions in particularmanners, this disclosure contemplates providing any suitableslot-suggestions in any suitable manner.

In particular embodiments, each of the one or more candidate hypotheses415 may additionally correspond to a subsequent entry associated withthe user input 405. As an example and not by way of limitation, an entrymay comprise a character, a word, a phrase, or a sentence. For example,the user input 405 may be “turn” and the subsequent entry may be “down”or “on the light”. In particular embodiments, the assistant system 140may store a plurality of entries in a range trie in the client system130 or in the data stores 310 of the assistant system 140. A range trieis a type of search tree, i.e., an ordered tree data structure used tostore a dynamic set or associative array. All the descendants of a nodehave a common prefix of the string associated with that node and theroot is associated with an empty string. Each entry in the range triemay be indexed by a prefix, thereby enabling the assistant system 140 tolook up candidate entries efficiently. In particular embodiments, therange trie may be an additional model input to the personalized languagemodel 410, which may speed up the look-up of candidate entries by theassistant system 140. Storing the candidate hypotheses 415 in a rangetrie in which the assistant system 140 can quickly look up candidatehypotheses 415 may be an effective solution for addressing the technicalchallenge of generating the candidate hypotheses 415 in real time.Although this disclosure describes particular entries stored inparticular tries in particular manners, this disclosure contemplates anysuitable entries stored in any suitable tries in any suitable manner.

In particular embodiments, the personalized language model 410 may bebased on a recurrent neural network. A recurrent neural network is aclass of artificial neural network which is able to characterize dynamictemporal behavior for a time sequence. A recurrent neural network canuse its internal state (memory) to process sequences of inputs, therebysuitable for language analysis such as speech recognition. In particularembodiments, training the recurrent neural network may comprise one ormore of selecting a size of each hidden layer of the recurrent neuralnetwork, adjusting one or more weights of the recurrent neural network,or validating the recurrent neural network by evaluating its performanceagainst a threshold accuracy level. The recurrent neural network maycompute a probability of a candidate hypothesis 415 matching a user'sintended subsequent entry following the user's initial input 405. Inparticular embodiments, the personalized language model 410 may betrained based on a plurality of training data 435 comprising one or moreof newsfeed posts associated with the first user, newsfeed commentsassociated with the first user, messages in one or more messaginginterfaces associated with the first user, data characterizing one ormore domains, dialog states of one or more dialog sessions associatedwith the first user, user profile data associated with the first user,task states associated with one or more tasks, any suitable data, or anycombination thereof. As an example and not by way of limitation, theuser profile data may comprise the user's contact information such asphone number (e.g., 650-123-4567). As a result, when receiving a userinput of “6 . . . ” the personalized language model 410 may generate acandidate hypothesis 415 of the phone number. In particular embodiments,a task state may indicate a status of a task such as “completed”,“pending”, “failed”, etc. As a result, the personalized language model410 trained based on task states may determine higher probabilities fortasks having “completed” task states but lower probabilities for taskshaving “failed” task states. Training the personalized language model410 based on task states may result in a technical advantage of guidingusers to tasks which the assistant system 140 excels to improve userexperience with the assistant system 140 as top ranked candidatehypotheses 415 tend to correspond to successful tasks executed by theassistant system 140 previously. In particular embodiments, using apersonalized language model 410 based on a recurrent neural networkwhich is trained based on a variety of training data 435 associated witha user may be an effective solution for addressing the technicalchallenge of accurately determining candidate hypotheses 415 based onthe partial request as the personalized language model 410 isdiscriminating in determining candidate hypotheses 415 by learningvarious information about the user from the training data 435 for acomprehensive understanding of the partial request. Although thisdisclosure describes training particular language models based onparticular training data in a particular manner, this disclosurecontemplates training any suitable language model based on any suitabletraining data in any suitable manner.

In particular embodiments, the one or more candidate hypotheses 415 maybe ranked based on a dialog state of a dialog session associated withthe user input 405. In particular embodiments, a dialog state mayindicate what the user wants from the assistant system 140. The dialogstate may comprise all that is used when the assistant system 140 makesits decision about what to communicate to the user next. In particularembodiments, the one or more candidate hypotheses 415 may be associatedwith one or more confidence scores, respectively. A confidence score mayindicate a likelihood that the candidate hypothesis 415 matches theuser's intended subsequent entry. In particular embodiments, the one ormore confidence scores may be calculated by the personalized languagemodel 410. Accordingly, the one or more candidate hypotheses 415 may beranked based on their respective confidence scores. In particularembodiments, the assistant system 140 may receive, from the clientsystem 130, an additional user input. The additional user input may beappended to the initial user input 405. As an example and not by way oflimitation, the initial user input 405 may comprise a character stringand the additional user input may comprise additional characters. Theadditional characters may be added to the character string of theinitial user input 405. In particular embodiments, the assistant system140 may then update, for the one or more candidate hypotheses 415, theone or more confidence scores based on the additional user input. Theassistant system 140 may further re-rank the one or more candidatehypotheses 415 based on the updated confidence scores. In particularembodiments, the assistant system 140 may keep updating the ranked listof candidate hypotheses 415 with newly scored candidate hypotheses 415until the user selects a candidate hypothesis 415 that matches theuser's intended subsequent entry. Ranking candidate hypotheses 415 basedon a dialog state and confidence scores determined by the personalizedlanguage model 410, and dynamically updating the confidence scores basedon additional user input to generate a list of ranked candidatehypotheses 415 may be effective solutions for addressing the technicalchallenge of presenting the most relevant candidate hypotheses 415 to auser which more correctly reflect a user's intention in a current dialogsession. Although this disclosure describes ranking particularhypotheses in particular manners, this disclosure contemplates rankingany suitable hypotheses in any suitable manner.

In particular embodiments, the assistant system 140 may apply a slidingwindow to the user input 405. A length of the sliding window maydetermine a percentage of the user input 405 to use as a model input tothe personalized language model 410. As an example and not by way oflimitation, the assistant system 140 may apply a sliding window to takethe most recent 20 characters or 7 words as a model input to thepersonalized language model 410 to generate candidate hypotheses 415. Inparticular embodiments, the assistant system 140 may apply a slidingwindow with varying lengths to dynamically adjust how to analyze theuser input 405 based on the confidence scores of the candidatehypotheses 415. In particular embodiments, the assistant system 140 maydetermine if at least one confidence score of the one or more confidencescores associated with the one or more candidate hypotheses 415 issmaller than a threshold score. Upon determining that at least oneconfidence score is smaller than the threshold score, the assistantsystem 140 may adjust the length of the sliding window. In particularembodiments, adjusting the length of the sliding window may compriseincreasing the length of the sliding window. In alternative embodiments,adjusting the length of the sliding window may comprise decreasing thelength of the sliding window. As an example and not by way oflimitation, the user input 405 may comprise “te” and the initial lengthof the sliding window may be one character. Accordingly, the assistantsystem 140 may determine a few candidate hypotheses 415 including “turnon . . . ”, “take . . . ”, “tell . . . ”, “text . . . ”, etc. One ofthese candidate hypotheses 415 may have a confidence score smaller thana threshold score. As a result, the assistant system 140 may adjust thelength by increasing it to two characters. Accordingly, the assistantsystem 140 may determine a few candidate hypotheses 415 including “teachme how to . . . ”, “tell . . . ”, “text . . . ”, etc., where all of themhave confidence scores larger than the threshold score. As anotherexample and not by way of limitation, the user input 405 may comprise“reso” and the initial length of the sliding window may be fourcharacters. Accordingly, the assistant system 140 may determine a fewcandidate hypotheses 415 including “resolve . . . ”, “resort to . . . ”,etc. However, “reso” may be a typo from the user, for which at least ofthe confidence scores associated with these candidate hypotheses 415 maybe smaller than a threshold score. For example, if the user did notmention anything about a question or a problem in the dialog sessionwith the assistant system 140, it is reasonable for the aforementionedcandidate hypotheses 415 to have small confidence scores. As a result,the assistant system 140 may adjust the length by decreasing it to threecharacters. Accordingly, the assistant system 140 may determine a fewcandidate hypotheses 415 including “reserve dinner at . . . ”, “reserveseat at . . . ”, etc., where all of them have confidence scores largerthan the threshold score. For example, the user has been talking aboutcelebrating his/her partner's birthday, it is reasonable for theassistant system 140 to assign high confidence scores for aforementionedcandidate hypotheses 415 based on “res” that is input to thepersonalized language model 410. Although this disclosure describesdynamically analyzing particular user input in a particular manner, thisdisclosure contemplates dynamically analyzing any suitable user input inany suitable manner.

In particular embodiments, the assistant system 140 may analyze the userinput 405 to generate the one or more candidate hypotheses 415corresponding to the partial request further based on one or morecontext-specific language models 420. The assistant system 140 may firstaccess, by a dialog engine 235, a dialog state of a dialog sessionassociated with the user input 405. The assistant system 140 may thenselect a particular context-specific language model 420 from the one ormore context-specific language models 420 based on the dialog state. Theassistant system 140 may further generate the one or more candidatehypotheses 415 based on the personalized language model 410 and theselected context-specific language model 420. In particular embodiments,the one or more context-specific language models 420 may be trainedbased on context-specific data 440. The context-specific data 440 maycomprise one or more of data associated with presences of the first userat particular locations, data associated with interactions of the firstuser with particular users, data associated with registrations of thefirst user at particular events, any suitable data, or any combinationthereof. As an example and not by way of limitation, a home-specificlanguage model 420 may be trained on data captured when the user iswithin a certain radius of his/her home address. As another example andnot by way of limitation, a work-specific language model 420 may betrained on data captured when the user is interacting withwork-colleagues via a messaging interface or within a certain radius ofhis/her work address. Accordingly, when the user is at home, theassistant system 140 may utilize the home-specific language model 420,while when the user is at work, the assistant system 140 may utilize thework-specific language model 420. Although this disclosure describesparticular context-specific language models in particular manners, thisdisclosure contemplates any suitable context-specific language model inany suitable manner.

In particular embodiments, the assistant system 140 may analyze the userinput 405 to generate the one or more candidate hypotheses 415corresponding to the partial request further based on one or more globallanguage models 425. The one or more global language models 425 may betrained based on global user data 450 which are data associated with aplurality of users of an online social network. In particularembodiments, the assistant system 140 may analyze the user input 405 togenerate the one or more candidate hypotheses 415 corresponding to thepartial request further based on one or more global context-specificlanguage models 430. The one or more context-specific language models430 may be trained based on global context-specific data 455 which aredata associated with the plurality of users of the online socialnetwork. As an example and not by way of limitation, the assistantsystem 140 may train a global language model 425 or globalcontext-specific language model 430 based on data associated with aplurality of users in a specific region or from a specific demographic.In particular embodiments, the assistant system 140 may utilize thepersonalized language model 410 in conjunction with the global languagemodels 425 and/or global context-specific language models 430. As anexample and not by way of limitation, if a user is travelling and inputs“hotels . . . ”, the assistant system 140 may determine an intent of[IN:reserve_hotel(hotel)] for the user input 405 based on a globallanguage model 425. The dialog engine 235 may then access aregion-specific (i.e., context-specific) global language model 430 togenerate a list of candidate slots of [SL:hotel(name)]. The dialogengine 235 may further utilize the personalized language model 410 torank the list of candidate slots. Continuing with the previous example,the dialog engine 235 may additionally use the global language model 425to determine another intent of [IN:get_directions(location)]. The dialogengine 235 may further use the personalized language model 410 togenerate a ranked list of candidate slots of [SL:location(coordinates)]corresponding to the list of candidate slots of [SL:hotel(name)],respectively. Using global language models 425 and/or global languagecontext-specific models 430 may be an effective solution for addressingthe technical challenge of the sparsity of data associated with anindividual user as the data associated with a plurality of users aresufficient to learn discriminative language models, which can enhancethe generation of candidate hypotheses 415 by the personalized languagemodel 410 alone. Although this disclosure describes particular globallanguage models in a particular manner, this disclosure contemplates anysuitable global language models in any suitable manner.

In particular embodiments, the assistant system 140 may recognize acategory of the user input 405 and generate candidate hypotheses 415accordingly based on the personalized language model 410. As an exampleand not by way of limitation, the user input 405 may comprise “s”, forwhich the assistant system 140 may recognize its category as “sendingmessage”. The assistant system 140 may then generate a candidatehypothesis 415 as “send a message to [SL:people(name)] at[SL:time(time)].” If the user selects this candidate hypothesis 415, theassistant system 140 may further generate a list of highly relevantfriends/family members for the slot of [SL:people(name)] and somepossible time for the slot of [SL:time(time)]. As another example andnot by way of limitation, the user input 405 may comprise “r”, for whichthe assistant system 140 may recognize its category as “reminder”. Theassistant system 140 may then generate candidate hypotheses 415including “remind me to call [SL:people(name)] when I arrive at[SL:location(name)]” and “remind me to call my mom on[SL:holiday(name)].” If the user selects “remind me to call[SL:people(name)] when I arrive at [SL:location(name)]”, the assistantsystem 140 may further generate a list of highly relevant friends/familymembers for the slot of [SL:people(name)] and a few most likelylocations for the slot of [SL:location(time)]. As another example andnot by way of limitation, the user input 405 may comprise “m”, for whichthe assistant system 140 may recognize its category as “reservation”.The assistant system 140 may then generate a candidate hypothesis 415 as“make a reservation at [SL:restaurant(name)] on [SL:date(date)].” If theuser selects this candidate hypothesis 415, the assistant system 140 mayfurther generate a list of nearby and/or good restaurants for the slotof [SL: restaurant (name)] and some possible dates for the slot of[SL:date(date)]. Although this disclosure describes generatingparticular candidate hypotheses corresponding to particular categoriesof user input in particular manners, this disclosure contemplatesgenerating any suitable candidate hypothesis corresponding to anysuitable category of user input in any suitable manner.

In particular embodiments, the assistant system 140 may provide nullstate candidate hypotheses 415 to a user. In particular embodiments,before the user begins to enter user input 405, the assistant system 140may generate candidate hypotheses 415 based on information from previoususer interactions with the assistant system 140. As an example and notby way of limitation, the previous user interactions may compriseinformation stored in the user context engine 225, user's searchhistory, user's request history, etc. The null-state candidatehypotheses 415 may be useful for attracting the user to use theassistant system 140. Although this disclosure describes providing nullstate candidate hypotheses in a particular manner, this disclosurecontemplates providing null state candidate hypotheses in any suitablemanner.

FIGS. 5A-5B illustrate example interactions with a user for suggestedauto-completions in a messaging interface 500. FIG. 5A illustrates anexample interaction with a user for suggested auto-completions in amessaging interface 500. As shown in FIG. 5A, the user may interact withthe assistant xbot 215 by using a keypad 505 on a client system 130associated with the user to generate user input 405. As an example andnot by way of limitation, the user input 405 may be “s”. The assistantsystem 140 may suggest a few auto-completions 510 including “send amessage to”, “set a timer”, and “share”. The assistant system 140 maypresent the suggested auto-completions 510 in a drop-down menu 515, inwhich the user can select one of them. For example, the user may select“set a timer”, for which the assistant system 140 may execute acorresponding task via an agent. In some cases, a suggestedauto-completion 510 selected by a user may be not readily executable andthe assistant system 140 may need additional user input from the user,which is exemplified in FIG. 5B. FIG. 5B illustrates an exampleinteraction with the user for suggested auto-completions 510 in themessaging interface 500 after the user selected a previously suggestedauto-completion 510. The user may have selected the previously suggested“send a message to” 520. The assistant system 140 may need to determinewhom the user wants to send the message to before executing acorresponding task. As shown in FIG. 5B, the assistant xbot 215 mayreceive an additional user input 525 of “r” from the user. The assistantsystem 140 may accordingly suggest auto-completions 510 including “senda message to Raymond” and “send a message to Roger”. The assistantsystem 140 may present the two suggested auto-completions 510 in thedrop-down menu 515, in which the user can select one of them. Forexample, the user may select “send a message to Roger”, for which theassistant system 140 may call an agent to send a message to Roger.Although this disclosure describes particular examples of interactionswith a user for suggested auto-completions in particular manners, thisdisclosure contemplates any suitable examples of interactions with auser for suggested auto-completions in any suitable manner.

FIG. 6 illustrates an example method 600 for suggesting auto-completions510. The method may begin at step 610, where the assistant system 140may receive, from a client system 130 associated with a first user, auser input 405 from the first user, wherein the user input 405 comprisesa partial request. At step 620, the assistant system 140 may analyze,based on a personalized language model 410, the user input 405 togenerate one or more candidate hypotheses 415 corresponding to thepartial request, wherein each of the one or more candidate hypotheses415 comprises one or more of an intent-suggestion or a slot-suggestion.At step 630, the assistant system 140 may send, to the client system130, instructions for presenting one or more suggested auto-completions510 corresponding to one or more of the candidate hypotheses 415,respectively, wherein each suggested auto-completion 510 comprises thepartial request and the corresponding candidate hypothesis 415. At step640, the assistant system 140 may receive, from the client system 130,an indication of a selection by the first user of a first suggestedauto-completion 510 of the one or more suggested auto-completions 510.At step 650, the assistant system 140 may execute, via one or moreagents, one or more tasks based on the first suggested auto-completion510 selected by the first user. Particular embodiments may repeat one ormore steps of the method of FIG. 6, where appropriate. Although thisdisclosure describes and illustrates particular steps of the method ofFIG. 6 as occurring in a particular order, this disclosure contemplatesany suitable steps of the method of FIG. 6 occurring in any suitableorder. Moreover, although this disclosure describes and illustrates anexample method for suggesting auto-completions, including the particularsteps of the method of FIG. 6, this disclosure contemplates any suitablemethod for suggesting auto-completions, including any suitable steps,which may include all, some, or none of the steps of the method of FIG.6, where appropriate. Furthermore, although this disclosure describesand illustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 6, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 6.

Social Graphs

FIG. 7 illustrates an example social graph 700. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 700 in one or more data stores. In particular embodiments,the social graph 700 may include multiple nodes—which may includemultiple user nodes 702 or multiple concept nodes 704—and multiple edges706 connecting the nodes. Each node may be associated with a uniqueentity (i.e., user or concept), each of which may have a uniqueidentifier (ID), such as a unique number or username. The example socialgraph 700 illustrated in FIG. 7 is shown, for didactic purposes, in atwo-dimensional visual map representation. In particular embodiments, asocial-networking system 160, a client system 130, an assistant system140, or a third-party system 170 may access the social graph 700 andrelated social-graph information for suitable applications. The nodesand edges of the social graph 700 may be stored as data objects, forexample, in a data store (such as a social-graph database). Such a datastore may include one or more searchable or queryable indexes of nodesor edges of the social graph 700.

In particular embodiments, a user node 702 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 702 corresponding tothe user, and store the user node 702 in one or more data stores. Usersand user nodes 702 described herein may, where appropriate, refer toregistered users and user nodes 702 associated with registered users. Inaddition or as an alternative, users and user nodes 702 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system 160. In particular embodiments, a user node 702may be associated with information provided by a user or informationgathered by various systems, including the social-networking system 160.As an example and not by way of limitation, a user may provide his orher name, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 702 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 702 may correspond to one or more webinterfaces.

In particular embodiments, a concept node 704 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node704 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 704 may be associated with one or more dataobjects corresponding to information associated with concept node 704.In particular embodiments, a concept node 704 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 700 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 170. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 704. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 702 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 704 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 704.

In particular embodiments, a concept node 704 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject (which may be implemented, for example, in JavaScript, AJAX, orPHP codes) representing an action or activity. As an example and not byway of limitation, a third-party web interface may include a selectableicon such as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 702 corresponding to the user and a conceptnode 704 corresponding to the third-party web interface or resource andstore edge 706 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 700 maybe connected to each other by one or more edges 706. An edge 706connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 706 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 706 connecting the first user's user node 702 to thesecond user's user node 702 in the social graph 700 and store edge 706as social-graph information in one or more of data stores 167. In theexample of FIG. 7, the social graph 700 includes an edge 706 indicatinga friend relation between user nodes 702 of user “A” and user “B” and anedge indicating a friend relation between user nodes 702 of user “C” anduser “B.” Although this disclosure describes or illustrates particularedges 706 with particular attributes connecting particular user nodes702, this disclosure contemplates any suitable edges 706 with anysuitable attributes connecting user nodes 702. As an example and not byway of limitation, an edge 706 may represent a friendship, familyrelationship, business or employment relationship, fan relationship(including, e.g., liking, etc.), follower relationship, visitorrelationship (including, e.g., accessing, viewing, checking-in, sharing,etc.), subscriber relationship, superior/subordinate relationship,reciprocal relationship, non-reciprocal relationship, another suitabletype of relationship, or two or more such relationships. Moreover,although this disclosure generally describes nodes as being connected,this disclosure also describes users or concepts as being connected.Herein, references to users or concepts being connected may, whereappropriate, refer to the nodes corresponding to those users or conceptsbeing connected in the social graph 700 by one or more edges 706.

In particular embodiments, an edge 706 between a user node 702 and aconcept node 704 may represent a particular action or activity performedby a user associated with user node 702 toward a concept associated witha concept node 704. As an example and not by way of limitation, asillustrated in FIG. 7, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to an edge type or subtype. A concept-profile interfacecorresponding to a concept node 704 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Imagine”) using a particular application (an online musicapplication). In this case, the social-networking system 160 may createa “listened” edge 706 and a “used” edge (as illustrated in FIG. 7)between user nodes 702 corresponding to the user and concept nodes 704corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 706 (asillustrated in FIG. 7) between concept nodes 704 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 706corresponds to an action performed by an external application on anexternal audio file (the song “Imagine”). Although this disclosuredescribes particular edges 706 with particular attributes connectinguser nodes 702 and concept nodes 704, this disclosure contemplates anysuitable edges 706 with any suitable attributes connecting user nodes702 and concept nodes 704. Moreover, although this disclosure describesedges between a user node 702 and a concept node 704 representing asingle relationship, this disclosure contemplates edges between a usernode 702 and a concept node 704 representing one or more relationships.As an example and not by way of limitation, an edge 706 may representboth that a user likes and has used at a particular concept.Alternatively, another edge 706 may represent each type of relationship(or multiples of a single relationship) between a user node 702 and aconcept node 704 (as illustrated in FIG. 7 between user node 702 foruser “E” and concept node 704).

In particular embodiments, the social-networking system 160 may createan edge 706 between a user node 702 and a concept node 704 in the socialgraph 700. As an example and not by way of limitation, a user viewing aconcept-profile interface (such as, for example, by using a web browseror a special-purpose application hosted by the user's client system 130)may indicate that he or she likes the concept represented by the conceptnode 704 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to the social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile interface. In response to the message, thesocial-networking system 160 may create an edge 706 between user node702 associated with the user and concept node 704, as illustrated by“like” edge 706 between the user and concept node 704. In particularembodiments, the social-networking system 160 may store an edge 706 inone or more data stores. In particular embodiments, an edge 706 may beautomatically formed by the social-networking system 160 in response toa particular user action. As an example and not by way of limitation, ifa first user uploads a picture, watches a movie, or listens to a song,an edge 706 may be formed between user node 702 corresponding to thefirst user and concept nodes 704 corresponding to those concepts.Although this disclosure describes forming particular edges 706 inparticular manners, this disclosure contemplates forming any suitableedges 706 in any suitable manner.

Vector Spaces and Embeddings

FIG. 8 illustrates an example view of a vector space 800. In particularembodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 800 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 800 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 800 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 800(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 810, 820, and 830 may be represented as points inthe vector space 800, as illustrated in FIG. 8. An n-gram may be mappedto a respective vector representation. As an example and not by way oflimitation, n-grams t₁ and t₂ may be mapped to vectors

and

in the vector space 800, respectively, by applying a function

defined by a dictionary, such that

=

(t₁) and

=

(t₂). As another example and not by way of limitation, a dictionarytrained to map text to a vector representation may be utilized, or sucha dictionary may be itself generated via training. As another exampleand not by way of limitation, a model, such as Word2vec, may be used tomap an n-gram to a vector representation in the vector space 800. Inparticular embodiments, an n-gram may be mapped to a vectorrepresentation in the vector space 800 by using a machine leaning model(e.g., a neural network). The machine learning model may have beentrained using a sequence of training data (e.g., a corpus of objectseach comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 800 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors

and

in the vector space 800, respectively, by applying a function

, such that

=

(e₁) and

=

(e₂). In particular embodiments, an object may be mapped to a vectorbased on one or more properties, attributes, or features of the object,relationships of the object with other objects, or any other suitableinformation associated with the object. As an example and not by way oflimitation, a function π^({right arrow over ( )}) may map objects tovectors by feature extraction, which may start from an initial set ofmeasured data and build derived values (e.g., features). As an exampleand not by way of limitation, an object comprising a video or an imagemay be mapped to a vector by using an algorithm to detect or isolatevarious desired portions or shapes of the object. Features used tocalculate the vector may be based on information obtained from edgedetection, corner detection, blob detection, ridge detection,scale-invariant feature transformation, edge direction, changingintensity, autocorrelation, motion detection, optical flow,thresholding, blob extraction, template matching, Hough transformation(e.g., lines, circles, ellipses, arbitrary shapes), or any othersuitable information. As another example and not by way of limitation,an object comprising audio data may be mapped to a vector based onfeatures such as a spectral slope, a tonality coefficient, an audiospectrum centroid, an audio spectrum envelope, a Mel-frequency cepstrum,or any other suitable information. In particular embodiments, when anobject has data that is either too large to be efficiently processed orcomprises redundant data, a function

may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction

may map an object e to a vector

(e) based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 800. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of

and

may be a cosine similarity

$\frac{v_{1}^{\rightharpoonup} \cdot v_{2}^{\rightharpoonup}}{{v_{1}^{\rightharpoonup}}{v_{2}^{\rightharpoonup}}}.$

As another example and not by way of limitation, a similarity metric of

and

may be a Euclidean distance ∥

−

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 800. As an example and not by way of limitation, vector810 and vector 820 may correspond to objects that are more similar toone another than the objects corresponding to vector 810 and vector 830,based on the distance between the respective vectors. Although thisdisclosure describes calculating a similarity metric between vectors ina particular manner, this disclosure contemplates calculating asimilarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 9 illustrates an example artificial neural network (“ANN”) 900. Inparticular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 900 may comprise an inputlayer 910, hidden layers 920, 930, 960, and an output layer 950. Eachlayer of the ANN 900 may comprise one or more nodes, such as a node 905or a node 915. In particular embodiments, each node of an ANN may beconnected to another node of the ANN. As an example and not by way oflimitation, each node of the input layer 910 may be connected to one ofmore nodes of the hidden layer 920. In particular embodiments, one ormore nodes may be a bias node (e.g., a node in a layer that is notconnected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 9 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 9 depicts a connection between each node of the inputlayer 910 and each node of the hidden layer 920, one or more nodes ofthe input layer 910 may not be connected to one or more nodes of thehidden layer 920.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 920 may comprise the output of one or morenodes of the input layer 910. As another example and not by way oflimitation, the input to each node of the output layer 950 may comprisethe output of one or more nodes of the hidden layer 960. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}\left( s_{k} \right)} = \frac{1}{1 + e^{- s_{k}}}},$

the hyperbolic tangent function

${{F_{k}\left( s_{k} \right)} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$

the rectifier F_(k)(s_(k))=max(0,s_(k)), or any other suitable functionF_(k)(s_(k)), where s_(k) may be the effective input to node k. Inparticular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection925 between the node 905 and the node 915 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 905 is used as an input to the node 915. As another exampleand not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j) (w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 900 and an expected output. As another example and notby way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 704 corresponding to a particular photo may havea privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 700. A privacy setting may be specifiedfor one or more edges 706 or edge-types of the social graph 700, or withrespect to one or more nodes 702, 704 or node-types of the social graph700. The privacy settings applied to a particular edge 706 connectingtwo nodes may control whether the relationship between the two entitiescorresponding to the nodes is visible to other users of the onlinesocial network. Similarly, the privacy settings applied to a particularnode may control whether the user or concept corresponding to the nodeis visible to other users of the online social network. As an exampleand not by way of limitation, a first user may share an object to thesocial-networking system 160. The object may be associated with aconcept node 704 connected to a user node 702 of the first user by anedge 706. The first user may specify privacy settings that apply to aparticular edge 706 connecting to the concept node 704 of the object, ormay specify privacy settings that apply to all edges 706 connecting tothe concept node 704. As another example and not by way of limitation,the first user may share a set of objects of a particular object-type(e.g., a set of images). The first user may specify privacy settingswith respect to all objects associated with the first user of thatparticular object-type as having a particular privacy setting (e.g.,specifying that all images posted by the first user are visible only tofriends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such voice recording may be used onlyfor a limited purpose (e.g., authentication, tagging the user inphotos), and further specify that such voice recording may not be sharedwith any third-party system 170 or used by other processes orapplications associated with the social-networking system 160.

Systems and Methods

FIG. 10 illustrates an example computer system 1000. In particularembodiments, one or more computer systems 1000 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1000 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1000 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1000.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1000. This disclosure contemplates computer system 1000 taking anysuitable physical form. As example and not by way of limitation,computer system 1000 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1000 may include one or more computersystems 1000; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1000 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1000 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1000 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1000 includes a processor1002, memory 1004, storage 1006, an input/output (I/O) interface 1008, acommunication interface 1010, and a bus 1012. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1002 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1002 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1004, or storage 1006; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1004, or storage 1006. In particularembodiments, processor 1002 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1002 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1002 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1004 or storage 1006, and the instruction caches may speed upretrieval of those instructions by processor 1002. Data in the datacaches may be copies of data in memory 1004 or storage 1006 forinstructions executing at processor 1002 to operate on; the results ofprevious instructions executed at processor 1002 for access bysubsequent instructions executing at processor 1002 or for writing tomemory 1004 or storage 1006; or other suitable data. The data caches mayspeed up read or write operations by processor 1002. The TLBs may speedup virtual-address translation for processor 1002. In particularembodiments, processor 1002 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1002 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1002 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1002. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1004 includes main memory for storinginstructions for processor 1002 to execute or data for processor 1002 tooperate on. As an example and not by way of limitation, computer system1000 may load instructions from storage 1006 or another source (such as,for example, another computer system 1000) to memory 1004. Processor1002 may then load the instructions from memory 1004 to an internalregister or internal cache. To execute the instructions, processor 1002may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1002 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1002 may then write one or more of those results to memory 1004. Inparticular embodiments, processor 1002 executes only instructions in oneor more internal registers or internal caches or in memory 1004 (asopposed to storage 1006 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1004 (asopposed to storage 1006 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1002 to memory 1004. Bus 1012 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1002 and memory 1004and facilitate accesses to memory 1004 requested by processor 1002. Inparticular embodiments, memory 1004 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1004 may include one ormore memories 1004, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1006 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1006 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1006 may include removable or non-removable (or fixed)media, where appropriate. Storage 1006 may be internal or external tocomputer system 1000, where appropriate. In particular embodiments,storage 1006 is non-volatile, solid-state memory. In particularembodiments, storage 1006 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1006taking any suitable physical form. Storage 1006 may include one or morestorage control units facilitating communication between processor 1002and storage 1006, where appropriate. Where appropriate, storage 1006 mayinclude one or more storages 1006. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1008 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1000 and one or more I/O devices. Computersystem 1000 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1000. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1008 for them. Where appropriate, I/Ointerface 1008 may include one or more device or software driversenabling processor 1002 to drive one or more of these I/O devices. I/Ointerface 1008 may include one or more I/O interfaces 1008, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1010 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1000 and one or more other computer systems 1000 or oneor more networks. As an example and not by way of limitation,communication interface 1010 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1010 for it. As an example and not by way oflimitation, computer system 1000 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1000 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1000 may include any suitable communicationinterface 1010 for any of these networks, where appropriate.Communication interface 1010 may include one or more communicationinterfaces 1010, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1012 includes hardware, software, or bothcoupling components of computer system 1000 to each other. As an exampleand not by way of limitation, bus 1012 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1012may include one or more buses 1012, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by a client system:receiving a first user input from a first user, wherein the first userinput comprises a partial request; presenting one or more suggestedintent auto-completions corresponding to the partial request; receivinga selection by the first user of a first suggested intentauto-completion of the one or more suggested intent auto-completions anda second user input; presenting one or more suggested slotauto-completions corresponding to one or more candidate slot-hypothesescorresponding to the second user input, respectively, wherein each ofthe one or more candidate slot-hypotheses comprise a slot-suggestion,and wherein each suggested slot auto-completion comprises the seconduser input and the corresponding candidate slot-hypothesis; receiving aselection by the first user of a first suggested slot auto-completion ofthe one or more suggested slot auto-completions; and presentingexecution results of one or more tasks corresponding to the firstsuggested intent auto-completion and the first suggested slotauto-completion.
 2. The method of claim 1, wherein the one or moresuggested intent auto-completions correspond to one or more candidateintent-hypotheses, wherein the one or more candidate intent-hypothesescorrespond to the partial request, wherein each of the one or morecandidate intent-hypotheses comprises an intent-suggestion, and whereineach of the one or more suggested intent auto-completions comprises thepartial request and the corresponding candidate intent-hypothesis. 3.The method of claim 2, further comprising analyzing, based on apersonalized language model, the first user input to generate the one ormore candidate intent-hypotheses corresponding to the partial request,wherein the analysis comprises: analyzing, based on the personalizedlanguage model, the first user input to determine one or more candidateintents.
 4. The method of claim 3, further comprising: presenting, atthe client system, a request for additional information from the firstuser; receiving, at the client system, an additional user input by thefirst user responsive to the request; and disambiguating, based on theadditional user input, the one or more candidate intents to determine atop candidate intent to provide as an intent-suggestion for one of thecandidate intent-hypotheses.
 5. The method of claim 3, wherein analyzingthe first user input to generate the one or more candidateintent-hypotheses corresponding to the partial request is further basedon one or more context-specific language models.
 6. The method of claim5, further comprising: accessing, by a dialog engine, a dialog state ofa dialog session associated with the first user input; selecting aparticular context-specific language model from the one or morecontext-specific language models based on the dialog state; andgenerating the one or more candidate intent-hypotheses based on thepersonalized language model and the selected context-specific model. 7.The method of claim 5, wherein the one or more context-specific languagemodels are trained based on context-specific data comprising one or moreof: data associated with presences of the first user at particularlocations; data associated with interactions of the first user withparticular users; or data associated with registrations of the firstuser at particular events.
 8. The method of claim 3, wherein analyzingthe first user input to generate the one or more candidateintent-hypotheses corresponding to the partial request is further basedon one or more global language models, wherein the one or more globallanguage models are trained based on data associated with a plurality ofusers of an online social network.
 9. The method of claim 3, whereinanalyzing the first user input to generate the one or more candidateintent-hypotheses corresponding to the partial request is further basedon one or more global context-specific language models.
 10. The methodof claim 1, further comprising analyzing, based on a personalizedlanguage model, the second user input to generate the one or morecandidate slot-hypotheses corresponding to the second user input,wherein the analysis comprises: analyzing, based on the personalizedlanguage model, the second user input to determine one or more candidateslots.
 11. The method of claim 10, further comprising: presenting, atthe client system, one or more slot-suggestions corresponding to the oneor more possible slots; and receiving, at the client system, a selectionby the first user of one of the one or more slot-suggestions, whereinthe selected slot suggestion is provided as a slot-suggestion for one ofthe candidate slot-hypotheses.
 12. The method of claim 10, furthercomprising: presenting, at the client system, a request for additionalinformation by the first user; receiving, at the client system, anadditional user input by the first user responsive to the request; anddisambiguating, based on the additional user input, the one or morecandidate slots to determine a top candidate slot to provide as aslot-suggestion for one of the candidate slot-hypotheses.
 13. The methodof claim 10, wherein the personalized language model is trained based ona plurality of training data comprising one or more of: newsfeed postsassociated with the first user; newsfeed comments associated with thefirst user; messages in one or more messaging interfaces associated withthe first user; data characterizing one or more domains; dialog statesof one or more dialog sessions associated with the first user; userprofile data associated with the first user; or task states associatedwith one or more tasks.
 14. The method of claim 10, wherein the one ormore candidate slot-hypotheses are associated with one or moreconfidence scores, respectively, wherein the one or more confidencescores are calculated by the personalized language model, and whereinthe one or more candidate slot-hypotheses are ranked based on theirrespective confidence scores.
 15. The method of claim 14, furthercomprising: receiving, at the client system, an additional user input,wherein the additional user input is appended to the second user input;updating, for the one or more candidate slot-hypotheses, the one or moreconfidence scores based on the additional user input; and re-ranking theone or more candidate slot-hypotheses based on the updated confidencescores.
 16. The method of claim 10, further comprising: applying asliding window to the second user input, wherein a length of the slidingwindow determines a percentage of the second user input to use as amodel input to the personalized language model.
 17. The method of claim16, further comprising: determining if at least one confidence score ofthe one or more confidence scores associated with the one or morecandidate slot-hypothesis is smaller than a threshold score; and upondetermining that at least one confidence score is smaller than thethreshold score, adjusting the length of the sliding window.
 18. Themethod of claim 1, wherein each of the one or more candidateslot-hypotheses corresponds to a subsequent entry associated with thesecond user input.
 19. One or more computer-readable non-transitorystorage media embodying software that is operable when executed to:receive a first user input from a first user, wherein the first userinput comprises a partial request; present one or more suggested intentauto-completions corresponding to the partial request; receive aselection by the first user of a first suggested intent auto-completionof the one or more suggested intent auto-completions and a second userinput; present one or more suggested slot auto-completions correspondingto one or more candidate slot-hypotheses corresponding to the seconduser input, respectively, wherein each of the one or more candidateslot-hypotheses comprise a slot-suggestion, and wherein each suggestedslot auto-completion comprises the second user input and thecorresponding candidate slot-hypothesis; receive a selection by thefirst user of a first suggested slot auto-completion of the one or moresuggested slot auto-completions; and present execution results of one ormore tasks corresponding to the first suggested intent auto-completionand the first suggested slot auto-completion.
 20. A system comprising:one or more processors; and a non-transitory memory coupled to theprocessors comprising instructions executable by the processors, theprocessors operable when executing the instructions to: receive a firstuser input from a first user, wherein the first user input comprises apartial request; present one or more suggested intent auto-completionscorresponding to the partial request; receive a selection by the firstuser of a first suggested intent auto-completion of the one or moresuggested intent auto-completions and a second user input; present oneor more suggested slot auto-completions corresponding to one or morecandidate slot-hypotheses corresponding to the second user input,respectively, wherein each of the one or more candidate slot-hypothesescomprise a slot-suggestion, and wherein each suggested slotauto-completion comprises the second user input and the correspondingcandidate slot-hypothesis; receive a selection by the first user of afirst suggested slot auto-completion of the one or more suggested slotauto-completions; and present execution results of one or more taskscorresponding to the first suggested intent auto-completion and thefirst suggested slot auto-completion.